1. Field of the Invention
The present invention relates to a data recognition device for recognizing, out of processing target data such as an image or voice, a recognition target data part corresponding to preset reference data.
2. Description of the Related Art
A processing of recognizing an image which has become a target of recognition, such as an object and a human face, by using the image as reference data can in principle be realized by a processing of calculating the degree of similarity of image data of a processing target to the pre-stored reference data.
However, actual images of recognition targets greatly differ depending on the environmental conditions such as their directions, distances, and lighting. Therefore, it is necessary to retain enormous quantity of data according to the degrees of freedom of these images, and the volume of calculation with them also results in an enormous quantity, therefore, realization is difficult.
Therefore, by carrying out a “normalization” processing for geometrically converting the recognition target image candidates to a predetermined positions, tilts, and sizes, the quantity of reference data of a reference target can be reduced, and the calculation volume according thereto can also be reduced.
Here, as a processing method for normalization, known is a method of extracting predetermined feature points from the processing target image data and adapting the feature points to a pre-arranged normalized image shape model. Here, for the feature points, a method using edge operators is commonly employed, however, for an object such as a human face whose surface shape is smooth, a clear edge cannot always be obtained, and because the edge itself is easily affected by the lighting condition, this is often inappropriate.
For this, a below identified document (hereinafter referred to as “document Rowley”) discloses a technique of directly detecting a deviation from a normalized image based on a shading pattern of image data of a processing target and carrying out a normalization processing by use of the detection result.
Document Rowley: Rotation Invariant Neural Network-Based Face Detection, H. A. Rowley, S. Baluja, and T. Kanade, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1998, pp. 38-44